Deep learning based human body segmentation for clothing fashion classification

The clothing fashion reflects an aesthetic trend for a period of time in dressing. To recognize the fashion time of a clothing is meaningful for both an individual and the industry. In this paper, under the assumption that the clothing fashion changes year by year, the fashion-time recognition problem is mapped into a clothing-fashion classification problem. Specifically, a deep-learning-based network is proposed which achieves accurate human body segmentation by fusing multi-scale convolutional features in a fully convolutional network, and then feature learning and fashion classification are performed on the segmented parts avoiding the influence of image background. In the experiments, 9,339 fashion images from 8 continuous years are collected to verify validity of the model proposed, and The performance shows the effectiveness of body segmentation and fashion classidication methods.